Overview

Brought to you by YData

Dataset statistics

Number of variables43
Number of observations213053
Missing cells321107
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.8 MiB
Average record size in memory309.0 B

Variable types

Numeric13
Boolean6
Text6
Unsupported2
Categorical14
DateTime2

Alerts

other_death_count has constant value "0" Constant
private_dr_fl has constant value "False" Constant
Is deleted has constant value "False" Constant
Crash ID is highly overall correlated with Is temporary recordHigh correlation
Is temporary record is highly overall correlated with Crash ID and 2 other fieldsHigh correlation
Law enforcement fatality count is highly overall correlated with crash_fatal_fl and 2 other fieldsHigh correlation
bicycle_serious_injury_count is highly overall correlated with sus_serious_injry_cntHigh correlation
crash_fatal_fl is highly overall correlated with Law enforcement fatality count and 4 other fieldsHigh correlation
crash_sev_id is highly overall correlated with crash_fatal_fl and 2 other fieldsHigh correlation
crash_speed_limit is highly overall correlated with onsys_flHigh correlation
death_cnt is highly overall correlated with Law enforcement fatality count and 3 other fieldsHigh correlation
motor_vehicle_death_count is highly overall correlated with Law enforcement fatality count and 2 other fieldsHigh correlation
motor_vehicle_serious_injury_count is highly overall correlated with sus_serious_injry_cntHigh correlation
nonincap_injry_cnt is highly overall correlated with crash_sev_id and 1 other fieldsHigh correlation
onsys_fl is highly overall correlated with crash_speed_limit and 1 other fieldsHigh correlation
pedestrian_death_count is highly overall correlated with crash_fatal_fl and 1 other fieldsHigh correlation
pedestrian_serious_injury_count is highly overall correlated with sus_serious_injry_cntHigh correlation
poss_injry_cnt is highly overall correlated with tot_injry_cntHigh correlation
road_constr_zone_fl is highly overall correlated with Is temporary recordHigh correlation
rpt_street_sfx is highly overall correlated with Is temporary record and 1 other fieldsHigh correlation
sus_serious_injry_cnt is highly overall correlated with bicycle_serious_injury_count and 2 other fieldsHigh correlation
tot_injry_cnt is highly overall correlated with crash_sev_id and 2 other fieldsHigh correlation
crash_fatal_fl is highly imbalanced (95.0%) Imbalance
road_constr_zone_fl is highly imbalanced (72.6%) Imbalance
death_cnt is highly imbalanced (97.8%) Imbalance
motor_vehicle_death_count is highly imbalanced (98.9%) Imbalance
bicycle_death_count is highly imbalanced (99.7%) Imbalance
bicycle_serious_injury_count is highly imbalanced (99.1%) Imbalance
pedestrian_death_count is highly imbalanced (98.7%) Imbalance
pedestrian_serious_injury_count is highly imbalanced (98.3%) Imbalance
motorcycle_death_count is highly imbalanced (99.4%) Imbalance
motorcycle_serious_injury_count is highly imbalanced (97.1%) Imbalance
other_serious_injury_count is highly imbalanced (> 99.9%) Imbalance
micromobility_serious_injury_count is highly imbalanced (99.8%) Imbalance
micromobility_death_count is highly imbalanced (> 99.9%) Imbalance
Is temporary record is highly imbalanced (> 99.9%) Imbalance
Law enforcement fatality count is highly imbalanced (98.7%) Imbalance
case_id has 2905 (1.4%) missing values Missing
rpt_block_num has 28653 (13.4%) missing values Missing
rpt_street_sfx has 65296 (30.6%) missing values Missing
latitude has 3730 (1.8%) missing values Missing
longitude has 3731 (1.8%) missing values Missing
point has 3731 (1.8%) missing values Missing
Reported street prefix has 213053 (100.0%) missing values Missing
ID has unique values Unique
rpt_block_num is an unsupported type, check if it needs cleaning or further analysis Unsupported
Reported street prefix is an unsupported type, check if it needs cleaning or further analysis Unsupported
crash_speed_limit has 11335 (5.3%) zeros Zeros
crash_sev_id has 15401 (7.2%) zeros Zeros
sus_serious_injry_cnt has 206553 (96.9%) zeros Zeros
nonincap_injry_cnt has 167293 (78.5%) zeros Zeros
poss_injry_cnt has 159987 (75.1%) zeros Zeros
non_injry_cnt has 36771 (17.3%) zeros Zeros
unkn_injry_cnt has 191633 (89.9%) zeros Zeros
tot_injry_cnt has 115902 (54.4%) zeros Zeros
motor_vehicle_serious_injury_count has 208893 (98.0%) zeros Zeros

Reproduction

Analysis started2024-12-04 17:36:10.146501
Analysis finished2024-12-04 17:40:21.122354
Duration4 minutes and 10.98 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Unique 

Distinct213053
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226675.19
Minimum2
Maximum1344636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:21.256354image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile20001.6
Q1103633
median220467
Q3338412
95-th percentile411616.8
Maximum1344636
Range1344634
Interquartile range (IQR)234779

Descriptive statistics

Standard deviation165138.66
Coefficient of variation (CV)0.7285255
Kurtosis14.732015
Mean226675.19
Median Absolute Deviation (MAD)117446
Skewness2.5013611
Sum4.8293829 × 1010
Variance2.7270776 × 1010
MonotonicityNot monotonic
2024-12-04T12:40:21.423356image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1344010 1
 
< 0.1%
1189 1
 
< 0.1%
6156 1
 
< 0.1%
9933 1
 
< 0.1%
912 1
 
< 0.1%
2282 1
 
< 0.1%
2680 1
 
< 0.1%
1294 1
 
< 0.1%
1661 1
 
< 0.1%
186 1
 
< 0.1%
Other values (213043) 213043
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
16 1
< 0.1%
19 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
41 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
ValueCountFrequency (%)
1344636 1
< 0.1%
1344635 1
< 0.1%
1344606 1
< 0.1%
1344605 1
< 0.1%
1344604 1
< 0.1%
1344601 1
< 0.1%
1344584 1
< 0.1%
1344514 1
< 0.1%
1344513 1
< 0.1%
1344512 1
< 0.1%

Crash ID
Real number (ℝ)

High correlation 

Distinct213051
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15818390
Minimum11152580
Maximum20522696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:21.588353image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum11152580
5-th percentile11799524
Q113662434
median15787017
Q317932416
95-th percentile19947354
Maximum20522696
Range9370116
Interquartile range (IQR)4269981

Descriptive statistics

Standard deviation2562075.9
Coefficient of variation (CV)0.16196818
Kurtosis-1.1135965
Mean15818390
Median Absolute Deviation (MAD)2130891
Skewness0.044457222
Sum3.3701238 × 1012
Variance6.5642329 × 1012
MonotonicityNot monotonic
2024-12-04T12:40:21.883870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13773522 1
 
< 0.1%
13852453 1
 
< 0.1%
13656594 1
 
< 0.1%
13688135 1
 
< 0.1%
13699847 1
 
< 0.1%
13665835 1
 
< 0.1%
13671384 1
 
< 0.1%
13634898 1
 
< 0.1%
13688474 1
 
< 0.1%
13705363 1
 
< 0.1%
Other values (213041) 213041
> 99.9%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
11152580 1
< 0.1%
11153849 1
< 0.1%
11154056 1
< 0.1%
11154058 1
< 0.1%
11154059 1
< 0.1%
11154064 1
< 0.1%
11154065 1
< 0.1%
11154069 1
< 0.1%
11154070 1
< 0.1%
11154229 1
< 0.1%
ValueCountFrequency (%)
20522696 1
< 0.1%
20522695 1
< 0.1%
20521817 1
< 0.1%
20521503 1
< 0.1%
20521477 1
< 0.1%
20521472 1
< 0.1%
20521470 1
< 0.1%
20521469 1
< 0.1%
20521468 1
< 0.1%
20521466 1
< 0.1%

crash_fatal_fl
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
False
211865 
True
 
1188
ValueCountFrequency (%)
False 211865
99.4%
True 1188
 
0.6%
2024-12-04T12:40:22.036877image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

case_id
Text

Missing 

Distinct209709
Distinct (%)99.8%
Missing2905
Missing (%)1.4%
Memory size1.6 MiB
2024-12-04T12:40:22.403879image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length20
Median length9
Mean length9.109185
Min length1

Characters and Unicode

Total characters1914277
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209275 ?
Unique (%)99.6%

Sample

1st row140221383
2nd row140940460
3rd row141440943
4th row140100795
5th row140291414
ValueCountFrequency (%)
tx 312
 
0.1%
10 53
 
< 0.1%
9
 
< 0.1%
go 6
 
< 0.1%
2023 5
 
< 0.1%
23 4
 
< 0.1%
100 4
 
< 0.1%
2024-799354 3
 
< 0.1%
130700836 3
 
< 0.1%
hcso 3
 
< 0.1%
Other values (209796) 210240
99.8%
2024-12-04T12:40:22.992870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 433455
22.6%
0 297887
15.6%
2 264384
13.8%
3 177072
9.3%
4 135834
 
7.1%
5 121773
 
6.4%
6 117871
 
6.2%
7 112777
 
5.9%
9 112588
 
5.9%
8 112085
 
5.9%
Other values (63) 28551
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1914277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 433455
22.6%
0 297887
15.6%
2 264384
13.8%
3 177072
9.3%
4 135834
 
7.1%
5 121773
 
6.4%
6 117871
 
6.2%
7 112777
 
5.9%
9 112588
 
5.9%
8 112085
 
5.9%
Other values (63) 28551
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1914277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 433455
22.6%
0 297887
15.6%
2 264384
13.8%
3 177072
9.3%
4 135834
 
7.1%
5 121773
 
6.4%
6 117871
 
6.2%
7 112777
 
5.9%
9 112588
 
5.9%
8 112085
 
5.9%
Other values (63) 28551
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1914277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 433455
22.6%
0 297887
15.6%
2 264384
13.8%
3 177072
9.3%
4 135834
 
7.1%
5 121773
 
6.4%
6 117871
 
6.2%
7 112777
 
5.9%
9 112588
 
5.9%
8 112085
 
5.9%
Other values (63) 28551
 
1.5%
Distinct81683
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:23.388871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length54
Median length46
Mean length18.193708
Min length2

Characters and Unicode

Total characters3876224
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56780 ?
Unique (%)26.7%

Sample

1st rowNOT REPORTED HWY
2nd row6600 N NOT REPORTED HWY
3rd rowSTONELAKE BLVD
4th rowE OLD EAST RIVERSIDE DR DR
5th row1000 NOT REPORTED HWY
ValueCountFrequency (%)
n 52456
 
5.7%
e 38614
 
4.2%
blvd 37424
 
4.1%
s 35680
 
3.9%
rd 30253
 
3.3%
st 28480
 
3.1%
w 27679
 
3.0%
ln 26449
 
2.9%
hwy 24220
 
2.6%
35 23566
 
2.6%
Other values (11192) 597036
64.8%
2024-12-04T12:40:23.951873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
708804
18.3%
0 379434
 
9.8%
E 222638
 
5.7%
R 221368
 
5.7%
N 195668
 
5.0%
S 188873
 
4.9%
D 157568
 
4.1%
A 148760
 
3.8%
L 138881
 
3.6%
T 132508
 
3.4%
Other values (32) 1381722
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3876224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
708804
18.3%
0 379434
 
9.8%
E 222638
 
5.7%
R 221368
 
5.7%
N 195668
 
5.0%
S 188873
 
4.9%
D 157568
 
4.1%
A 148760
 
3.8%
L 138881
 
3.6%
T 132508
 
3.4%
Other values (32) 1381722
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3876224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
708804
18.3%
0 379434
 
9.8%
E 222638
 
5.7%
R 221368
 
5.7%
N 195668
 
5.0%
S 188873
 
4.9%
D 157568
 
4.1%
A 148760
 
3.8%
L 138881
 
3.6%
T 132508
 
3.4%
Other values (32) 1381722
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3876224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
708804
18.3%
0 379434
 
9.8%
E 222638
 
5.7%
R 221368
 
5.7%
N 195668
 
5.0%
S 188873
 
4.9%
D 157568
 
4.1%
A 148760
 
3.8%
L 138881
 
3.6%
T 132508
 
3.4%
Other values (32) 1381722
35.6%
Distinct65487
Distinct (%)30.7%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
2024-12-04T12:40:24.351871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length64
Median length47
Mean length15.904948
Min length1

Characters and Unicode

Total characters3388565
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46177 ?
Unique (%)21.7%

Sample

1st rowNOT REPORTED
2nd rowST JOHNS
3rd rowNOT REPORTED
4th rowPLEASANT VALLEY RD
5th rowE E 8TH TO IH 35 NB RAMP
ValueCountFrequency (%)
dr 41493
 
5.5%
st 41037
 
5.4%
e 39186
 
5.2%
ln 32883
 
4.4%
rd 29891
 
4.0%
blvd 24826
 
3.3%
w 23860
 
3.2%
n 18791
 
2.5%
s 15493
 
2.1%
ave 10806
 
1.4%
Other values (10948) 477156
63.2%
2024-12-04T12:40:24.950870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
542371
16.0%
R 252198
 
7.4%
0 244024
 
7.2%
E 235625
 
7.0%
N 178210
 
5.3%
D 172028
 
5.1%
A 166175
 
4.9%
S 165438
 
4.9%
L 163835
 
4.8%
T 157715
 
4.7%
Other values (32) 1110946
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3388565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
542371
16.0%
R 252198
 
7.4%
0 244024
 
7.2%
E 235625
 
7.0%
N 178210
 
5.3%
D 172028
 
5.1%
A 166175
 
4.9%
S 165438
 
4.9%
L 163835
 
4.8%
T 157715
 
4.7%
Other values (32) 1110946
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3388565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
542371
16.0%
R 252198
 
7.4%
0 244024
 
7.2%
E 235625
 
7.0%
N 178210
 
5.3%
D 172028
 
5.1%
A 166175
 
4.9%
S 165438
 
4.9%
L 163835
 
4.8%
T 157715
 
4.7%
Other values (32) 1110946
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3388565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
542371
16.0%
R 252198
 
7.4%
0 244024
 
7.2%
E 235625
 
7.0%
N 178210
 
5.3%
D 172028
 
5.1%
A 166175
 
4.9%
S 165438
 
4.9%
L 163835
 
4.8%
T 157715
 
4.7%
Other values (32) 1110946
32.8%

rpt_block_num
Unsupported

Missing  Rejected  Unsupported 

Missing28653
Missing (%)13.4%
Memory size1.6 MiB
Distinct13570
Distinct (%)6.4%
Missing1
Missing (%)< 0.1%
Memory size1.6 MiB
2024-12-04T12:40:25.363872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length52
Median length43
Mean length10.213145
Min length1

Characters and Unicode

Total characters2175931
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8131 ?
Unique (%)3.8%

Sample

1st rowNOT REPORTED
2nd rowNOT REPORTED
3rd rowSTONELAKE
4th rowOLD EAST RIVERSIDE DR
5th rowNOT REPORTED
ValueCountFrequency (%)
35 23552
 
5.0%
ih 22580
 
4.8%
sb 20526
 
4.3%
nb 19990
 
4.2%
svrd 18495
 
3.9%
not 14254
 
3.0%
reported 14254
 
3.0%
n 13646
 
2.9%
mopac 11621
 
2.5%
blvd 10062
 
2.1%
Other values (5796) 303694
64.3%
2024-12-04T12:40:25.924869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
259622
 
11.9%
E 180409
 
8.3%
R 180176
 
8.3%
A 142664
 
6.6%
S 138624
 
6.4%
N 135865
 
6.2%
T 109133
 
5.0%
O 106989
 
4.9%
D 90188
 
4.1%
L 89047
 
4.1%
Other values (27) 743214
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2175931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
259622
 
11.9%
E 180409
 
8.3%
R 180176
 
8.3%
A 142664
 
6.6%
S 138624
 
6.4%
N 135865
 
6.2%
T 109133
 
5.0%
O 106989
 
4.9%
D 90188
 
4.1%
L 89047
 
4.1%
Other values (27) 743214
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2175931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
259622
 
11.9%
E 180409
 
8.3%
R 180176
 
8.3%
A 142664
 
6.6%
S 138624
 
6.4%
N 135865
 
6.2%
T 109133
 
5.0%
O 106989
 
4.9%
D 90188
 
4.1%
L 89047
 
4.1%
Other values (27) 743214
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2175931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
259622
 
11.9%
E 180409
 
8.3%
R 180176
 
8.3%
A 142664
 
6.6%
S 138624
 
6.4%
N 135865
 
6.2%
T 109133
 
5.0%
O 106989
 
4.9%
D 90188
 
4.1%
L 89047
 
4.1%
Other values (27) 743214
34.2%

rpt_street_sfx
Categorical

High correlation  Missing 

Distinct20
Distinct (%)< 0.1%
Missing65296
Missing (%)30.6%
Memory size1.6 MiB
BLVD
27362 
RD
23498 
ST
22454 
LN
20886 
HWY
18054 
Other values (15)
35503 

Length

Max length4
Median length2
Mean length2.6909182
Min length1

Characters and Unicode

Total characters397602
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowHWY
2nd rowHWY
3rd rowBLVD
4th rowDR
5th rowHWY

Common Values

ValueCountFrequency (%)
BLVD 27362
12.8%
RD 23498
 
11.0%
ST 22454
 
10.5%
LN 20886
 
9.8%
HWY 18054
 
8.5%
DR 16518
 
7.8%
EXPY 7801
 
3.7%
AVE 5731
 
2.7%
PKWY 2341
 
1.1%
TRL 816
 
0.4%
Other values (10) 2296
 
1.1%
(Missing) 65296
30.6%

Length

2024-12-04T12:40:26.078870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blvd 27362
18.5%
rd 23498
15.9%
st 22454
15.2%
ln 20886
14.1%
hwy 18054
12.2%
dr 16518
11.2%
expy 7801
 
5.3%
ave 5731
 
3.9%
pkwy 2341
 
1.6%
trl 816
 
0.6%
Other values (9) 2295
 
1.6%

Most occurring characters

ValueCountFrequency (%)
D 67378
16.9%
L 49816
12.5%
R 41184
10.4%
V 33207
8.4%
Y 29170
7.3%
B 27362
6.9%
T 23372
 
5.9%
S 22454
 
5.6%
W 21369
 
5.4%
N 20886
 
5.3%
Other values (12) 61404
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 397602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 67378
16.9%
L 49816
12.5%
R 41184
10.4%
V 33207
8.4%
Y 29170
7.3%
B 27362
6.9%
T 23372
 
5.9%
S 22454
 
5.6%
W 21369
 
5.4%
N 20886
 
5.3%
Other values (12) 61404
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 397602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 67378
16.9%
L 49816
12.5%
R 41184
10.4%
V 33207
8.4%
Y 29170
7.3%
B 27362
6.9%
T 23372
 
5.9%
S 22454
 
5.6%
W 21369
 
5.4%
N 20886
 
5.3%
Other values (12) 61404
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 397602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 67378
16.9%
L 49816
12.5%
R 41184
10.4%
V 33207
8.4%
Y 29170
7.3%
B 27362
6.9%
T 23372
 
5.9%
S 22454
 
5.6%
W 21369
 
5.4%
N 20886
 
5.3%
Other values (12) 61404
15.4%

crash_speed_limit
Real number (ℝ)

High correlation  Zeros 

Distinct34
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.094831
Minimum-1
Maximum85
Zeros11335
Zeros (%)5.3%
Negative38258
Negative (%)18.0%
Memory size1.6 MiB
2024-12-04T12:40:26.207871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q125
median40
Q355
95-th percentile65
Maximum85
Range86
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.704258
Coefficient of variation (CV)0.64694024
Kurtosis-0.93428711
Mean35.094831
Median Absolute Deviation (MAD)15
Skewness-0.4484523
Sum7477024
Variance515.48335
MonotonicityNot monotonic
2024-12-04T12:40:26.350869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
-1 38258
18.0%
35 32661
15.3%
45 27618
13.0%
55 20076
9.4%
30 18787
8.8%
65 16011
7.5%
40 14029
 
6.6%
50 11431
 
5.4%
0 11335
 
5.3%
60 10217
 
4.8%
Other values (24) 12629
 
5.9%
ValueCountFrequency (%)
-1 38258
18.0%
0 11335
 
5.3%
5 96
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
10 238
 
0.1%
15 522
 
0.2%
20 442
 
0.2%
24 2
 
< 0.1%
25 3204
 
1.5%
ValueCountFrequency (%)
85 7
 
< 0.1%
80 219
 
0.1%
79 1
 
< 0.1%
75 1078
 
0.5%
70 6791
3.2%
66 1
 
< 0.1%
65 16011
7.5%
64 1
 
< 0.1%
60 10217
4.8%
58 1
 
< 0.1%

road_constr_zone_fl
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
False
203006 
True
 
10045
(Missing)
 
2
ValueCountFrequency (%)
False 203006
95.3%
True 10045
 
4.7%
(Missing) 2
 
< 0.1%
2024-12-04T12:40:26.477872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

latitude
Real number (ℝ)

Missing 

Distinct128171
Distinct (%)61.2%
Missing3730
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean30.299313
Minimum30.098737
Maximum30.511625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:26.620872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum30.098737
5-th percentile30.178762
Q130.233837
median30.28555
Q330.363435
95-th percentile30.442425
Maximum30.511625
Range0.41288743
Interquartile range (IQR)0.12959862

Descriptive statistics

Standard deviation0.082710499
Coefficient of variation (CV)0.0027297813
Kurtosis-0.78597703
Mean30.299313
Median Absolute Deviation (MAD)0.062043034
Skewness0.28830095
Sum6342343.2
Variance0.0068410266
MonotonicityNot monotonic
2024-12-04T12:40:26.768868image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.44802363 157
 
0.1%
30.33965264 156
 
0.1%
30.33882963 156
 
0.1%
30.21720989 148
 
0.1%
30.24915123 139
 
0.1%
30.27753478 133
 
0.1%
30.32556534 132
 
0.1%
30.33917999 131
 
0.1%
30.20236015 122
 
0.1%
30.1984147 116
 
0.1%
Other values (128161) 207933
97.6%
(Missing) 3730
 
1.8%
ValueCountFrequency (%)
30.0987373 1
< 0.1%
30.09889195 1
< 0.1%
30.0989809 1
< 0.1%
30.09899192 1
< 0.1%
30.09902058 1
< 0.1%
30.09906538 1
< 0.1%
30.09907731 1
< 0.1%
30.09908481 1
< 0.1%
30.09911847 1
< 0.1%
30.09919882 1
< 0.1%
ValueCountFrequency (%)
30.51162473 2
< 0.1%
30.50985091 1
< 0.1%
30.50981473 1
< 0.1%
30.50939476 1
< 0.1%
30.50938474 1
< 0.1%
30.50938473 1
< 0.1%
30.50775084 1
< 0.1%
30.50774627 1
< 0.1%
30.50758422 1
< 0.1%
30.50744995 1
< 0.1%

longitude
Real number (ℝ)

Missing 

Distinct127996
Distinct (%)61.1%
Missing3731
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean-97.738393
Minimum-97.927173
Maximum-97.570148
Zeros0
Zeros (%)0.0%
Negative209322
Negative (%)98.2%
Memory size1.6 MiB
2024-12-04T12:40:27.029871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-97.927173
5-th percentile-97.83319
Q1-97.769527
median-97.736748
Q3-97.702491
95-th percentile-97.660881
Maximum-97.570148
Range0.35702563
Interquartile range (IQR)0.067036559

Descriptive statistics

Standard deviation0.052345893
Coefficient of variation (CV)-0.00053557145
Kurtosis0.25304048
Mean-97.738393
Median Absolute Deviation (MAD)0.033464015
Skewness-0.27115181
Sum-20458796
Variance0.0027400925
MonotonicityNot monotonic
2024-12-04T12:40:27.191871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.79304577 157
 
0.1%
-97.69958145 156
 
0.1%
-97.70074124 156
 
0.1%
-97.70010376 151
 
0.1%
-97.75176842 148
 
0.1%
-97.80529785 139
 
0.1%
-97.67331696 133
 
0.1%
-97.74629246 132
 
0.1%
-97.74195246 130
 
0.1%
-97.63785553 122
 
0.1%
Other values (127986) 207898
97.6%
(Missing) 3731
 
1.8%
ValueCountFrequency (%)
-97.92717344 1
< 0.1%
-97.92678889 1
< 0.1%
-97.92658994 2
< 0.1%
-97.92649002 1
< 0.1%
-97.92493512 1
< 0.1%
-97.92453454 1
< 0.1%
-97.92448327 1
< 0.1%
-97.92442406 1
< 0.1%
-97.92431681 1
< 0.1%
-97.92417591 1
< 0.1%
ValueCountFrequency (%)
-97.57014781 1
< 0.1%
-97.57024774 1
< 0.1%
-97.57029177 1
< 0.1%
-97.57040391 1
< 0.1%
-97.57054204 1
< 0.1%
-97.57054233 1
< 0.1%
-97.57071655 1
< 0.1%
-97.57074071 1
< 0.1%
-97.57075015 1
< 0.1%
-97.57102709 1
< 0.1%

crash_sev_id
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4565484
Minimum0
Maximum5
Zeros15401
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:27.320870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6270647
Coefficient of variation (CV)0.4707195
Kurtosis-0.85837561
Mean3.4565484
Median Absolute Deviation (MAD)2
Skewness-0.55038845
Sum736428
Variance2.6473396
MonotonicityNot monotonic
2024-12-04T12:40:27.438871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 99700
46.8%
3 46006
21.6%
2 44400
20.8%
0 15401
 
7.2%
1 6358
 
3.0%
4 1188
 
0.6%
ValueCountFrequency (%)
0 15401
 
7.2%
1 6358
 
3.0%
2 44400
20.8%
3 46006
21.6%
4 1188
 
0.6%
5 99700
46.8%
ValueCountFrequency (%)
5 99700
46.8%
4 1188
 
0.6%
3 46006
21.6%
2 44400
20.8%
1 6358
 
3.0%
0 15401
 
7.2%

sus_serious_injry_cnt
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034822321
Minimum0
Maximum14
Zeros206553
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:27.543873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21399697
Coefficient of variation (CV)6.1453965
Kurtosis187.86025
Mean0.034822321
Median Absolute Deviation (MAD)0
Skewness9.2193063
Sum7419
Variance0.045794704
MonotonicityNot monotonic
2024-12-04T12:40:27.654871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 206553
96.9%
1 5792
 
2.7%
2 571
 
0.3%
3 94
 
< 0.1%
4 28
 
< 0.1%
5 12
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 206553
96.9%
1 5792
 
2.7%
2 571
 
0.3%
3 94
 
< 0.1%
4 28
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
5 12
 
< 0.1%
4 28
 
< 0.1%
3 94
 
< 0.1%
2 571
 
0.3%
1 5792
 
2.7%
0 206553
96.9%

nonincap_injry_cnt
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28519195
Minimum0
Maximum57
Zeros167293
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:27.770869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum57
Range57
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65318385
Coefficient of variation (CV)2.2903306
Kurtosis284.14343
Mean0.28519195
Median Absolute Deviation (MAD)0
Skewness6.2198557
Sum60761
Variance0.42664914
MonotonicityNot monotonic
2024-12-04T12:40:27.884870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 167293
78.5%
1 35285
 
16.6%
2 7577
 
3.6%
3 1932
 
0.9%
4 620
 
0.3%
5 206
 
0.1%
6 78
 
< 0.1%
7 34
 
< 0.1%
9 11
 
< 0.1%
8 9
 
< 0.1%
Other values (6) 8
 
< 0.1%
ValueCountFrequency (%)
0 167293
78.5%
1 35285
 
16.6%
2 7577
 
3.6%
3 1932
 
0.9%
4 620
 
0.3%
5 206
 
0.1%
6 78
 
< 0.1%
7 34
 
< 0.1%
8 9
 
< 0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 11
 
< 0.1%
8 9
 
< 0.1%
7 34
< 0.1%
6 78
< 0.1%

poss_injry_cnt
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35555941
Minimum0
Maximum21
Zeros159987
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:28.002886image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75623409
Coefficient of variation (CV)2.1268853
Kurtosis23.104304
Mean0.35555941
Median Absolute Deviation (MAD)0
Skewness3.438377
Sum75753
Variance0.57189
MonotonicityNot monotonic
2024-12-04T12:40:28.130871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 159987
75.1%
1 38228
 
17.9%
2 10112
 
4.7%
3 2966
 
1.4%
4 1025
 
0.5%
5 417
 
0.2%
6 180
 
0.1%
7 72
 
< 0.1%
8 31
 
< 0.1%
9 16
 
< 0.1%
Other values (9) 19
 
< 0.1%
ValueCountFrequency (%)
0 159987
75.1%
1 38228
 
17.9%
2 10112
 
4.7%
3 2966
 
1.4%
4 1025
 
0.5%
5 417
 
0.2%
6 180
 
0.1%
7 72
 
< 0.1%
8 31
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 3
 
< 0.1%
10 6
 
< 0.1%
9 16
< 0.1%

non_injry_cnt
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.848831
Minimum0
Maximum64
Zeros36771
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:28.258871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6903839
Coefficient of variation (CV)0.91429874
Kurtosis86.820858
Mean1.848831
Median Absolute Deviation (MAD)1
Skewness4.8993455
Sum393899
Variance2.8573977
MonotonicityNot monotonic
2024-12-04T12:40:28.411870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 62168
29.2%
1 62043
29.1%
0 36771
17.3%
3 27601
13.0%
4 12579
 
5.9%
5 6162
 
2.9%
6 2826
 
1.3%
7 1295
 
0.6%
8 692
 
0.3%
9 347
 
0.2%
Other values (41) 569
 
0.3%
ValueCountFrequency (%)
0 36771
17.3%
1 62043
29.1%
2 62168
29.2%
3 27601
13.0%
4 12579
 
5.9%
5 6162
 
2.9%
6 2826
 
1.3%
7 1295
 
0.6%
8 692
 
0.3%
9 347
 
0.2%
ValueCountFrequency (%)
64 1
 
< 0.1%
56 1
 
< 0.1%
53 1
 
< 0.1%
50 2
< 0.1%
49 1
 
< 0.1%
48 2
< 0.1%
47 1
 
< 0.1%
46 3
< 0.1%
44 2
< 0.1%
43 3
< 0.1%

unkn_injry_cnt
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11817247
Minimum0
Maximum41
Zeros191633
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:28.555876image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41091328
Coefficient of variation (CV)3.4772335
Kurtosis541.21411
Mean0.11817247
Median Absolute Deviation (MAD)0
Skewness10.203928
Sum25177
Variance0.16884973
MonotonicityNot monotonic
2024-12-04T12:40:28.671871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 191633
89.9%
1 18921
 
8.9%
2 1767
 
0.8%
3 471
 
0.2%
4 153
 
0.1%
5 64
 
< 0.1%
6 18
 
< 0.1%
7 12
 
< 0.1%
8 5
 
< 0.1%
14 2
 
< 0.1%
Other values (6) 7
 
< 0.1%
ValueCountFrequency (%)
0 191633
89.9%
1 18921
 
8.9%
2 1767
 
0.8%
3 471
 
0.2%
4 153
 
0.1%
5 64
 
< 0.1%
6 18
 
< 0.1%
7 12
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
< 0.1%
8 5
 
< 0.1%
7 12
< 0.1%
6 18
< 0.1%

tot_injry_cnt
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67557368
Minimum0
Maximum61
Zeros115902
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:28.783880image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum61
Range61
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97369716
Coefficient of variation (CV)1.4412894
Kurtosis80.914582
Mean0.67557368
Median Absolute Deviation (MAD)0
Skewness3.4629966
Sum143933
Variance0.94808616
MonotonicityNot monotonic
2024-12-04T12:40:28.908869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 115902
54.4%
1 67054
31.5%
2 20094
 
9.4%
3 6254
 
2.9%
4 2203
 
1.0%
5 868
 
0.4%
6 371
 
0.2%
7 163
 
0.1%
8 70
 
< 0.1%
9 31
 
< 0.1%
Other values (10) 43
 
< 0.1%
ValueCountFrequency (%)
0 115902
54.4%
1 67054
31.5%
2 20094
 
9.4%
3 6254
 
2.9%
4 2203
 
1.0%
5 868
 
0.4%
6 371
 
0.2%
7 163
 
0.1%
8 70
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
18 1
 
< 0.1%
15 3
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 10
< 0.1%
11 7
< 0.1%
10 14
< 0.1%

death_cnt
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
211898 
1
 
1096
2
 
54
3
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 211898
99.5%
1 1096
 
0.5%
2 54
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Length

2024-12-04T12:40:29.034871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:29.153871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 211898
99.5%
1 1096
 
0.5%
2 54
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 211898
99.5%
1 1096
 
0.5%
2 54
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 211898
99.5%
1 1096
 
0.5%
2 54
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 211898
99.5%
1 1096
 
0.5%
2 54
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 211898
99.5%
1 1096
 
0.5%
2 54
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:29.397873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length92
Median length79
Mean length27.046608
Min length7

Characters and Unicode

Total characters5762361
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowMotorcycle
2nd rowPassenger car
3rd rowPassenger car
4th rowPassenger car
5th rowPassenger car
ValueCountFrequency (%)
passenger 284197
32.3%
car 166293
18.9%
vehicle 133858
15.2%
large 117904
13.4%
108914
 
12.4%
motor 15954
 
1.8%
15954
 
1.8%
other 15954
 
1.8%
other/unknown 6750
 
0.8%
motorcycle 5746
 
0.7%
Other values (6) 9376
 
1.1%
2024-12-04T12:40:29.694870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 996864
17.3%
667847
11.6%
r 618272
10.7%
s 573794
10.0%
a 573454
10.0%
g 402101
7.0%
c 319805
 
5.5%
n 309507
 
5.4%
P 171335
 
3.0%
h 156562
 
2.7%
Other values (23) 972820
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5762361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 996864
17.3%
667847
11.6%
r 618272
10.7%
s 573794
10.0%
a 573454
10.0%
g 402101
7.0%
c 319805
 
5.5%
n 309507
 
5.4%
P 171335
 
3.0%
h 156562
 
2.7%
Other values (23) 972820
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5762361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 996864
17.3%
667847
11.6%
r 618272
10.7%
s 573794
10.0%
a 573454
10.0%
g 402101
7.0%
c 319805
 
5.5%
n 309507
 
5.4%
P 171335
 
3.0%
h 156562
 
2.7%
Other values (23) 972820
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5762361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 996864
17.3%
667847
11.6%
r 618272
10.7%
s 573794
10.0%
a 573454
10.0%
g 402101
7.0%
c 319805
 
5.5%
n 309507
 
5.4%
P 171335
 
3.0%
h 156562
 
2.7%
Other values (23) 972820
16.9%

point
Text

Missing 

Distinct130099
Distinct (%)62.2%
Missing3731
Missing (%)1.8%
Memory size1.6 MiB
2024-12-04T12:40:30.103872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length45
Median length32
Mean length33.769355
Min length22

Characters and Unicode

Total characters7068669
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113027 ?
Unique (%)54.0%

Sample

1st rowPOINT (-97.7898706 30.19389906)
2nd rowPOINT (-97.71668542 30.40834659)
3rd rowPOINT (-97.73741764 30.240121)
4th rowPOINT (-97.76466175 30.17777241)
5th rowPOINT (-97.68975365 30.37589167)
ValueCountFrequency (%)
point 209322
33.3%
30.44802363 157
 
< 0.1%
97.79304577 157
 
< 0.1%
97.7007412424805 156
 
< 0.1%
30.3396526378689 156
 
< 0.1%
97.6995814479184 156
 
< 0.1%
30.3388296344939 156
 
< 0.1%
97.70010376 151
 
< 0.1%
97.7517684165415 148
 
< 0.1%
30.217209891268 148
 
< 0.1%
Other values (256157) 417259
66.4%
2024-12-04T12:40:30.596874image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 715556
 
10.1%
3 597170
 
8.4%
9 526222
 
7.4%
0 482287
 
6.8%
2 438720
 
6.2%
4 427772
 
6.1%
. 418644
 
5.9%
418644
 
5.9%
6 379781
 
5.4%
8 348264
 
4.9%
Other values (10) 2315609
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7068669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 715556
 
10.1%
3 597170
 
8.4%
9 526222
 
7.4%
0 482287
 
6.8%
2 438720
 
6.2%
4 427772
 
6.1%
. 418644
 
5.9%
418644
 
5.9%
6 379781
 
5.4%
8 348264
 
4.9%
Other values (10) 2315609
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7068669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 715556
 
10.1%
3 597170
 
8.4%
9 526222
 
7.4%
0 482287
 
6.8%
2 438720
 
6.2%
4 427772
 
6.1%
. 418644
 
5.9%
418644
 
5.9%
6 379781
 
5.4%
8 348264
 
4.9%
Other values (10) 2315609
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7068669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 715556
 
10.1%
3 597170
 
8.4%
9 526222
 
7.4%
0 482287
 
6.8%
2 438720
 
6.2%
4 427772
 
6.1%
. 418644
 
5.9%
418644
 
5.9%
6 379781
 
5.4%
8 348264
 
4.9%
Other values (10) 2315609
32.8%

motor_vehicle_death_count
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212541 
1
 
464
2
 
43
3
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212541
99.8%
1 464
 
0.2%
2 43
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Length

2024-12-04T12:40:30.747873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:30.868871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212541
99.8%
1 464
 
0.2%
2 43
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212541
99.8%
1 464
 
0.2%
2 43
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212541
99.8%
1 464
 
0.2%
2 43
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212541
99.8%
1 464
 
0.2%
2 43
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212541
99.8%
1 464
 
0.2%
2 43
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

motor_vehicle_serious_injury_count
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023365078
Minimum0
Maximum7
Zeros208893
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-12-04T12:40:30.974871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18133541
Coefficient of variation (CV)7.7609587
Kurtosis145.37583
Mean0.023365078
Median Absolute Deviation (MAD)0
Skewness10.236579
Sum4978
Variance0.032882529
MonotonicityNot monotonic
2024-12-04T12:40:31.191871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 208893
98.0%
1 3524
 
1.7%
2 508
 
0.2%
3 89
 
< 0.1%
4 26
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 208893
98.0%
1 3524
 
1.7%
2 508
 
0.2%
3 89
 
< 0.1%
4 26
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 12
 
< 0.1%
4 26
 
< 0.1%
3 89
 
< 0.1%
2 508
 
0.2%
1 3524
 
1.7%
0 208893
98.0%

bicycle_death_count
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
213013 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 213013
> 99.9%
1 40
 
< 0.1%

Length

2024-12-04T12:40:31.318871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:31.427872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 213013
> 99.9%
1 40
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 213013
> 99.9%
1 40
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 213013
> 99.9%
1 40
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 213013
> 99.9%
1 40
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 213013
> 99.9%
1 40
 
< 0.1%

bicycle_serious_injury_count
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212644 
1
 
405
2
 
2
3
 
1
14
 
1

Length

Max length2
Median length1
Mean length1.0000047
Min length1

Characters and Unicode

Total characters213054
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212644
99.8%
1 405
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
14 1
 
< 0.1%

Length

2024-12-04T12:40:31.543875image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:31.664872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212644
99.8%
1 405
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
14 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212644
99.8%
1 406
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213054
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212644
99.8%
1 406
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213054
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212644
99.8%
1 406
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213054
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212644
99.8%
1 406
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

pedestrian_death_count
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212631 
1
 
415
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212631
99.8%
1 415
 
0.2%
2 7
 
< 0.1%

Length

2024-12-04T12:40:31.791872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:31.902871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212631
99.8%
1 415
 
0.2%
2 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212631
99.8%
1 415
 
0.2%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212631
99.8%
1 415
 
0.2%
2 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212631
99.8%
1 415
 
0.2%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212631
99.8%
1 415
 
0.2%
2 7
 
< 0.1%

pedestrian_serious_injury_count
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212185 
1
 
851
2
 
15
3
 
1
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212185
99.6%
1 851
 
0.4%
2 15
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Length

2024-12-04T12:40:32.033869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:32.176871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212185
99.6%
1 851
 
0.4%
2 15
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212185
99.6%
1 851
 
0.4%
2 15
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212185
99.6%
1 851
 
0.4%
2 15
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212185
99.6%
1 851
 
0.4%
2 15
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212185
99.6%
1 851
 
0.4%
2 15
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

motorcycle_death_count
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212877 
1
 
173
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212877
99.9%
1 173
 
0.1%
2 3
 
< 0.1%

Length

2024-12-04T12:40:32.303869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:32.428877image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212877
99.9%
1 173
 
0.1%
2 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212877
99.9%
1 173
 
0.1%
2 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212877
99.9%
1 173
 
0.1%
2 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212877
99.9%
1 173
 
0.1%
2 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212877
99.9%
1 173
 
0.1%
2 3
 
< 0.1%

motorcycle_serious_injury_count
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212020 
1
 
999
2
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212020
99.5%
1 999
 
0.5%
2 34
 
< 0.1%

Length

2024-12-04T12:40:32.540871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:32.653871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212020
99.5%
1 999
 
0.5%
2 34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212020
99.5%
1 999
 
0.5%
2 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212020
99.5%
1 999
 
0.5%
2 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212020
99.5%
1 999
 
0.5%
2 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212020
99.5%
1 999
 
0.5%
2 34
 
< 0.1%

other_death_count
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
213053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 213053
100.0%

Length

2024-12-04T12:40:32.812869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:32.919870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 213053
100.0%

Most occurring characters

ValueCountFrequency (%)
0 213053
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 213053
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 213053
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 213053
100.0%

other_serious_injury_count
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
213046 
1
 
6
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 213046
> 99.9%
1 6
 
< 0.1%
3 1
 
< 0.1%

Length

2024-12-04T12:40:33.028870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:33.143870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 213046
> 99.9%
1 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 213046
> 99.9%
1 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 213046
> 99.9%
1 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 213046
> 99.9%
1 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 213046
> 99.9%
1 6
 
< 0.1%
3 1
 
< 0.1%

onsys_fl
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
False
109129 
True
103924 
ValueCountFrequency (%)
False 109129
51.2%
True 103924
48.8%
2024-12-04T12:40:33.279870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

private_dr_fl
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
False
213053 
ValueCountFrequency (%)
False 213053
100.0%
2024-12-04T12:40:33.379872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

micromobility_serious_injury_count
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
213009 
1
 
42
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 213009
> 99.9%
1 42
 
< 0.1%
2 2
 
< 0.1%

Length

2024-12-04T12:40:33.499872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:33.606882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 213009
> 99.9%
1 42
 
< 0.1%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 213009
> 99.9%
1 42
 
< 0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 213009
> 99.9%
1 42
 
< 0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 213009
> 99.9%
1 42
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 213009
> 99.9%
1 42
 
< 0.1%
2 2
 
< 0.1%

micromobility_death_count
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
213047 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 213047
> 99.9%
1 6
 
< 0.1%

Length

2024-12-04T12:40:33.728873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:33.876881image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 213047
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 213047
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 213047
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 213047
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 213047
> 99.9%
1 6
 
< 0.1%
Distinct208462
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Minimum2010-01-01 00:59:00
Maximum2024-11-19 01:36:00
2024-12-04T12:40:34.003869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:34.162872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct208462
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Minimum2010-01-01 06:59:00
Maximum2024-11-19 07:36:00
2024-12-04T12:40:34.314872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:34.503871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Is deleted
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
False
213053 
ValueCountFrequency (%)
False 213053
100.0%
2024-12-04T12:40:34.641921image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Is temporary record
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
False
213051 
True
 
2
ValueCountFrequency (%)
False 213051
> 99.9%
True 2
 
< 0.1%
2024-12-04T12:40:34.743874image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Law enforcement fatality count
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
212433 
1
 
590
2
 
28
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters213053
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 212433
99.7%
1 590
 
0.3%
2 28
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-12-04T12:40:34.858873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:40:34.974872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 212433
99.7%
1 590
 
0.3%
2 28
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 212433
99.7%
1 590
 
0.3%
2 28
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212433
99.7%
1 590
 
0.3%
2 28
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212433
99.7%
1 590
 
0.3%
2 28
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212433
99.7%
1 590
 
0.3%
2 28
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Reported street prefix
Unsupported

Missing  Rejected  Unsupported 

Missing213053
Missing (%)100.0%
Memory size1.6 MiB

Interactions

2024-12-04T12:40:15.025231image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.000534image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:50.882172image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:52.742311image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:55.474618image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:57.712162image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.686313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.481735image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:03.598046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:06.185679image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:08.348685image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:10.869841image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:12.864423image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:15.205230image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.158679image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.021217image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:52.963312image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:55.717202image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:57.893174image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.834433image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.624741image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:03.808044image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:06.362678image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:08.534686image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.054841image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.020433image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:15.377249image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.287693image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.147236image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:53.169310image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:55.892042image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.043163image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.967510image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.770754image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:03.967046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:06.539676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:08.697681image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.188863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.154458image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:15.536253image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.418678image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.274190image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:53.542317image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:56.032041image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.175162image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.098506image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.894752image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:04.143048image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:06.701676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:08.938675image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.315976image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.296800image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:15.812306image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.563690image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.448197image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:53.748417image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:56.183052image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.360186image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.224511image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:02.035752image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:04.336046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:06.881685image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:09.231682image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.446975image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.458007image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:15.990340image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.702478image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.578189image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:54.047874image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:56.329061image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.535264image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.376034image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:02.174776image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:04.556046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:07.051684image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:09.583675image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.690153image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.650764image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:16.172341image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.832477image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.706195image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:54.259567image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:56.477066image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.666271image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.509723image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:02.314290image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:04.775046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:07.211677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:09.750678image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.833186image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.819743image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:16.355338image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:49.974488image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.858276image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:54.467569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:56.629094image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.821274image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.638721image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:02.575178image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:04.978075image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:07.375676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:09.921677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:11.998179image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:13.987741image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:16.522341image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:50.103483image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:51.990302image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:54.664106image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:56.782117image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:58.962278image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.771719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:02.756181image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:05.205087image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:07.529683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:10.092676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:12.162177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:14.151739image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:16.684337image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:50.232514image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:52.116286image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:54.836566image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:57.056126image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.109268image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:00.895722image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:02.908212image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:05.388598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:07.678679image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:10.257683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:12.300183image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:14.319745image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:16.854337image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:50.400554image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:52.245291image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:54.992565image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:57.204132image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.256270image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.044280image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:03.103322image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:05.606678image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:07.848684image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:10.423944image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:12.437180image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:14.492739image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:17.018345image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:50.551176image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:52.367313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:55.147253image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:57.344129image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.390275image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.174727image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:03.256320image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:05.812677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:08.004676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:10.554538image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:12.568178image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:14.652924image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:17.186337image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:50.708162image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:52.558316image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:55.309093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:57.499159image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:39:59.541315image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:01.321727image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:03.451839image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:05.989679image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:08.162682image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:10.697843image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:12.704913image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:40:14.837927image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-04T12:40:35.103872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Crash IDIDIs temporary recordLaw enforcement fatality countbicycle_death_countbicycle_serious_injury_countcrash_fatal_flcrash_sev_idcrash_speed_limitdeath_cntlatitudelongitudemicromobility_death_countmicromobility_serious_injury_countmotor_vehicle_death_countmotor_vehicle_serious_injury_countmotorcycle_death_countmotorcycle_serious_injury_countnon_injry_cntnonincap_injry_cntonsys_flother_serious_injury_countpedestrian_death_countpedestrian_serious_injury_countposs_injry_cntroad_constr_zone_flrpt_street_sfxsus_serious_injry_cnttot_injry_cntunkn_injry_cnt
Crash ID1.000-0.2471.0000.0300.0000.0010.0180.0320.0370.009-0.0040.0280.0070.0100.005-0.0030.0000.003-0.015-0.0290.0450.0000.0120.004-0.0200.1040.0280.001-0.036-0.023
ID-0.2471.0000.0460.0150.0160.0000.013-0.0370.0650.0050.011-0.0100.0040.0020.0010.0050.0000.000-0.0230.0330.0390.0000.0060.0000.0200.0470.0170.0060.0400.007
Is temporary record1.0000.0461.0000.0580.0560.0000.0310.0410.0000.0420.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0001.0001.0000.0000.0000.000
Law enforcement fatality count0.0300.0150.0581.0000.1490.0000.7210.3610.0130.6860.0050.0090.1010.0000.6050.0240.2310.0000.0000.0000.0210.0000.3720.0200.0000.0090.0040.0140.0000.000
bicycle_death_count0.0000.0160.0560.1491.0000.0000.1810.1830.0080.1910.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.000
bicycle_serious_injury_count0.0010.0000.0000.0000.0001.0000.0000.1250.0100.0000.0050.0060.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0060.0330.5030.1670.000
crash_fatal_fl0.0180.0130.0310.7210.1810.0001.0001.0000.0260.9860.0150.0230.0650.0000.6550.0520.3840.0050.0030.0000.0230.0000.5950.0290.0090.0030.0130.0420.0040.000
crash_sev_id0.032-0.0370.0410.3610.1830.1251.0001.0000.0260.4930.024-0.0500.0710.0580.328-0.2080.2710.2780.473-0.5680.0420.0230.4210.181-0.2340.0130.036-0.264-0.654-0.455
crash_speed_limit0.0370.0650.0000.0130.0080.0100.0260.0261.0000.0140.003-0.0350.0000.0060.0130.0220.0020.0080.044-0.0130.5820.0000.0080.0130.0220.1320.2350.0040.009-0.028
death_cnt0.0090.0050.0420.6860.1910.0000.9860.4930.0141.0000.0070.0120.0740.0000.8970.0390.3260.0010.0000.0000.0240.0000.5030.0150.0000.0030.0030.0320.0000.000
latitude-0.0040.0110.0040.0050.0000.0050.0150.0240.0030.0071.0000.3180.0000.0100.006-0.0010.0000.005-0.006-0.0220.1460.0000.0080.0070.0150.0740.209-0.006-0.006-0.016
longitude0.028-0.0100.0000.0090.0050.0060.023-0.050-0.0350.0120.3181.0000.0000.0100.0120.0170.0020.003-0.0140.0100.1280.0000.0110.0060.0130.0800.1690.0140.0210.066
micromobility_death_count0.0070.0040.0000.1010.0000.0000.0650.0710.0000.0740.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
micromobility_serious_injury_count0.0100.0020.0000.0000.0000.0000.0000.0580.0060.0000.0100.0100.0001.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0010.0150.0410.0000.000
motor_vehicle_death_count0.0050.0010.0000.6050.0000.0000.6550.3280.0130.8970.0060.0120.0000.0001.0000.0500.0000.0000.0000.0000.0210.0000.0000.0230.0060.0000.0060.0400.0040.000
motor_vehicle_serious_injury_count-0.0030.0050.0000.0240.0000.0000.052-0.2080.0220.039-0.0010.0170.0000.0000.0501.0000.0000.003-0.0900.0170.0230.0120.0000.007-0.0120.0000.0140.7970.176-0.016
motorcycle_death_count0.0000.0000.0000.2310.0000.0000.3840.2710.0020.3260.0000.0020.0000.0000.0000.0001.0000.0150.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.000
motorcycle_serious_injury_count0.0030.0000.0000.0000.0000.0000.0050.2780.0080.0010.0050.0030.0000.0000.0000.0030.0151.0000.0000.0000.0100.0000.0000.0000.0020.0050.0090.1680.0000.000
non_injry_cnt-0.015-0.0230.0000.0000.0000.0000.0030.4730.0440.000-0.006-0.0140.0000.0000.000-0.0900.0000.0001.000-0.2420.0330.0000.0000.000-0.2230.0100.009-0.113-0.384-0.242
nonincap_injry_cnt-0.0290.0330.0000.0000.0000.0000.000-0.568-0.0130.000-0.0220.0100.0000.0000.0000.0170.0000.000-0.2421.0000.0030.0000.0000.000-0.1350.0000.000-0.0110.580-0.030
onsys_fl0.0450.0390.0000.0210.0000.0200.0230.0420.5820.0240.1460.1280.0000.0050.0210.0230.0020.0100.0330.0031.0000.0050.0130.0210.0130.1180.6340.0090.0080.004
other_serious_injury_count0.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0051.0000.0000.0000.0000.0000.0000.0700.0000.000
pedestrian_death_count0.0120.0060.0340.3720.0000.0000.5950.4210.0080.5030.0080.0110.0000.0000.0000.0000.0000.0000.0000.0000.0130.0001.0000.0060.0000.0030.0000.0000.0000.000
pedestrian_serious_injury_count0.0040.0000.0000.0200.0000.0000.0290.1810.0130.0150.0070.0060.0000.0000.0230.0070.0000.0000.0000.0000.0210.0000.0061.0000.0000.0100.0230.5090.0290.000
poss_injry_cnt-0.0200.0200.0000.0000.0000.0000.009-0.2340.0220.0000.0150.0130.0000.0000.006-0.0120.0000.002-0.223-0.1350.0130.0000.0000.0001.0000.0000.007-0.0420.656-0.055
road_constr_zone_fl0.1040.0471.0000.0090.0020.0060.0030.0130.1320.0030.0740.0800.0000.0010.0000.0000.0000.0050.0100.0000.1180.0000.0030.0100.0001.0000.1310.0000.0000.004
rpt_street_sfx0.0280.0171.0000.0040.0000.0330.0130.0360.2350.0030.2090.1690.0000.0150.0060.0140.0000.0090.0090.0000.6340.0000.0000.0230.0070.1311.0000.0100.0000.000
sus_serious_injry_cnt0.0010.0060.0000.0140.0000.5030.042-0.2640.0040.032-0.0060.0140.0000.0410.0400.7970.0000.168-0.113-0.0110.0090.0700.0000.509-0.0420.0000.0101.0000.197-0.013
tot_injry_cnt-0.0360.0400.0000.0000.0000.1670.004-0.6540.0090.000-0.0060.0210.0000.0000.0040.1760.0000.000-0.3840.5800.0080.0000.0000.0290.6560.0000.0000.1971.000-0.068
unkn_injry_cnt-0.0230.0070.0000.0000.0000.0000.000-0.455-0.0280.000-0.0160.0660.0000.0000.000-0.0160.0000.000-0.242-0.0300.0040.0000.0000.000-0.0550.0040.000-0.013-0.0681.000

Missing values

2024-12-04T12:40:17.548345image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-04T12:40:18.950353image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-04T12:40:20.564356image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDCrash IDcrash_fatal_flcase_idPrimary addressSecondary addressrpt_block_numrpt_street_namerpt_street_sfxcrash_speed_limitroad_constr_zone_fllatitudelongitudecrash_sev_idsus_serious_injry_cntnonincap_injry_cntposs_injry_cntnon_injry_cntunkn_injry_cnttot_injry_cntdeath_cntunits_involvedpointmotor_vehicle_death_countmotor_vehicle_serious_injury_countbicycle_death_countbicycle_serious_injury_countpedestrian_death_countpedestrian_serious_injury_countmotorcycle_death_countmotorcycle_serious_injury_countother_death_countother_serious_injury_countonsys_flprivate_dr_flmicromobility_serious_injury_countmicromobility_death_countCrash timestamp (US/Central)Crash timestampIs deletedIs temporary recordLaw enforcement fatality countReported street prefix
0118913663405.0False140221383NOT REPORTED HWYNOT REPORTEDNaNNOT REPORTEDHWY65.0FalseNaNNaN50001000MotorcycleNaN0000000000TrueFalse0001/22/2014 07:08:00 PM01/23/2014 01:08:00 AMFalseFalse0NaN
1615613773522.0False1409404606600 N NOT REPORTED HWYST JOHNS6600NOT REPORTEDHWY-1.0FalseNaNNaN50001000Passenger carNaN0000000000TrueFalse0004/04/2014 07:46:00 AM04/04/2014 12:46:00 PMFalseFalse0NaN
2993313852453.0False141440943STONELAKE BLVDNOT REPORTEDNaNSTONELAKEBLVD45.0FalseNaNNaN30020020Passenger carNaN0000000000FalseFalse0005/24/2014 12:04:00 PM05/24/2014 05:04:00 PMFalseFalse0NaN
391213656594.0False140100795E OLD EAST RIVERSIDE DR DRPLEASANT VALLEY RDNaNOLD EAST RIVERSIDE DRDR30.0FalseNaNNaN50001000Passenger carNaN0000000000FalseFalse0001/10/2014 12:18:00 PM01/10/2014 06:18:00 PMFalseFalse0NaN
4228213688135.0False1402914141000 NOT REPORTED HWYE E 8TH TO IH 35 NB RAMP1000NOT REPORTEDHWY55.0FalseNaNNaN50002000Passenger carNaN0000000000TrueFalse0001/29/2014 06:44:00 PM01/30/2014 12:44:00 AMFalseFalse0NaN
5268013699847.0False1404011212500 NOT REPORTED RDPEARCE LN2500NOT REPORTEDRD50.0FalseNaNNaN20210030Large passenger vehicleNaN0000000000TrueFalse0002/09/2014 04:26:00 PM02/09/2014 10:26:00 PMFalseFalse0NaN
6129413665835.0False1400807837500 NOT REPORTED EXPYW WILLIAM CANNON DR7500NOT REPORTEDEXPY-1.0False30.193899-97.78987150001000Passenger carPOINT (-97.7898706 30.19389906)0000000000TrueFalse0001/08/2014 01:30:00 PM01/08/2014 07:30:00 PMFalseFalse0NaN
7166113671384.0False14007123311900 NOT REPORTED EXPYDUVAL RD11900NOT REPORTEDEXPY-1.0False30.408347-97.71668520200020Motorcycle & Passenger carPOINT (-97.71668542 30.40834659)0000000000TrueFalse0001/07/2014 06:30:00 PM01/08/2014 12:30:00 AMFalseFalse0NaN
818613634898.0False1400310481600 NOT REPORTEDWOODLAND AVE1600NOT REPORTEDNaN60.0False30.240121-97.73741850002000Large passenger vehicle & Passenger carPOINT (-97.73741764 30.240121)0000000000TrueFalse0001/03/2014 02:26:00 PM01/03/2014 08:26:00 PMFalseFalse0NaN
9235613688474.0False1404402362306 PATSY PKWYSTONLEIGH PL2306PATSYPKWY30.0False30.177772-97.76466230010010Large passenger vehicle & Passenger carPOINT (-97.76466175 30.17777241)0000000000FalseFalse0002/13/2014 04:16:00 AM02/13/2014 10:16:00 AMFalseFalse0NaN
IDCrash IDcrash_fatal_flcase_idPrimary addressSecondary addressrpt_block_numrpt_street_namerpt_street_sfxcrash_speed_limitroad_constr_zone_fllatitudelongitudecrash_sev_idsus_serious_injry_cntnonincap_injry_cntposs_injry_cntnon_injry_cntunkn_injry_cnttot_injry_cntdeath_cntunits_involvedpointmotor_vehicle_death_countmotor_vehicle_serious_injury_countbicycle_death_countbicycle_serious_injury_countpedestrian_death_countpedestrian_serious_injury_countmotorcycle_death_countmotorcycle_serious_injury_countother_death_countother_serious_injury_countonsys_flprivate_dr_flmicromobility_serious_injury_countmicromobility_death_countCrash timestamp (US/Central)Crash timestampIs deletedIs temporary recordLaw enforcement fatality countReported street prefix
213043134286820490420.0False243121174900 E 11TH ST1100 N IH 35 SVRD900.011THST30.0False30.270404-97.73290550004000Large passenger vehicle & Passenger carPOINT (-97.73290462 30.27040381)0000000000TrueFalse0011/07/2024 06:57:00 PM11/08/2024 12:57:00 AMFalseFalse0NaN
213044134251020482739.0False2430803406300 S S MOPAC EXPY SVRD SB EXPY4200 W WILLIAM CANNON DR DR6300.0S MOPAC EXPY SVRD SBEXPY55.0False30.220927-97.83605650001000Passenger carPOINT (-97.83605635 30.22092654)0000000000TrueFalse0011/03/2024 02:12:00 AM11/03/2024 08:12:00 AMFalseFalse0NaN
213045134244420483934.0False2431000584200 N IH 35 UPPER DECK NB HWY800 N 183 HWY HWY4200.0IH 35 UPPER DECK NBHWY60.0False30.345518-97.69609850002000Large passenger vehiclePOINT (-97.69609821 30.34551781)0000000000TrueFalse0011/05/2024 01:17:00 AM11/05/2024 07:17:00 AMFalseFalse0NaN
213046134219220477538.0False2430506974600 N N IH 35 SB HWY4500 N AIRPORT TO IH 35 SVRD SB RAMP BLVD4600.0N IH 35 SBHWY60.0FalseNaNNaN50002000Large passenger vehicle & Passenger carNaN0000000000TrueFalse0010/31/2024 12:17:00 PM10/31/2024 05:17:00 PMFalseFalse0NaN
213047134332420497780.0False243140952200 E 15TH ST1500 SAN JACINTO ST200.015THST35.0False30.276165-97.73712250003000Large passenger vehicle & MotorcyclePOINT (-97.73712246 30.27616478)0000000000FalseFalse0011/09/2024 02:54:00 PM11/09/2024 08:54:00 PMFalseFalse0NaN
213048134186620471775.0False2412502651800 E E BEN WHITE EB TO S 35 SB RAMP BLVD1800 W E BEN WHITE EB TO S 35 SB RAMP BLVD1800.0E BEN WHITE EB TO S 35 SB RAMPBLVD55.0False30.216639-97.74755811000010Passenger carPOINT (-97.7475576281157 30.216638652391396)0100000000FalseFalse0005/04/2024 03:36:00 AM05/04/2024 08:36:00 AMFalseFalse0NaN
213049134383020507809.0False2432007611100 W STASSNEY LN5600 EMERALD FOREST DR1100.0STASSNEYLN35.0False30.210793-97.78709220112020Large passenger vehicle & Passenger carPOINT (-97.78709195 30.21079276)0000000000FalseFalse0011/15/2024 12:52:00 PM11/15/2024 06:52:00 PMFalseFalse0NaN
213050134156920468979.0False24291024011000 N N IH 35 NB800 E E BRAKER LN11000.0N IH 35 NBNaN65.0True30.376012-97.67675350006000Large passenger vehiclePOINT (-97.67675332 30.376012)0000000000TrueFalse0010/17/2024 05:39:00 AM10/17/2024 10:39:00 AMFalseFalse0NaN
213051134418120513007.0False24324110913800 N 183 NB TO LAKELINE MALL RAMPN N 183 NB TO LAKELINE MALL RAMP DR13800.0N 183 NB TO LAKELINE MALL RAMPNaN65.0False30.463085-97.79539250008000Large passenger vehiclePOINT (-97.79539243 30.46308472)0000000000FalseFalse0011/16/2024 06:03:00 PM11/17/2024 12:03:00 AMFalseFalse0NaN
213052134401020510487.0False2431815557300 S IH 35 NBFOREMOST DR DR7300.0S IH 35 NBNaN70.0False30.174809-97.78096650003000Large passenger vehiclePOINT (-97.78096562 30.17480917)0000000000TrueFalse0011/13/2024 11:03:00 PM11/14/2024 05:03:00 AMFalseFalse0NaN